Dashboard

Column

Map strains

View grid display

About this dashboard

Column

About this project

What is Pyblastr?

A database of information on characterized fungal strains of Pyricularia, the genus name for a complex of species that cause blast symptoms on both leaves and reproductive organs of agriculturally important Poacea (eg. wheat blast) and grasses in general, including turfgrass.

What is it useful for?

The dashboard contains tabular and graphical elements for displaying information for each georeferenced strain, thus allowing to track temporal and spatial spread of species and host-specific lineages (or pathotypes) isolated from both wheat and grasses.

Who are we?

Pyblastr was created during the conduction of PhD research projects at the Plant Disease Epidemiology Laboratory (Dr. Del Ponte) in collaboration with Dr. Farman (University of Kentucky). The first version was released on February 2020 and is expected to be continuously updated.

May I contribute data?

Yes! definitely this is an open database that we would like to extend its utility by allowing other researches to contribution data to populate the global map. Get in touch () to receive instructions on how to participate and include the data in a Google sheets.

Key wheat blast resources

Reviews
Wheat blast diseases: danger on the move
Wheat blast: past, present and future
Wheat blast: from its origins in South America to its emergence as a global threat

Websites
Open Wheat Blast Initiative
Cimmyt CABi

Webinars
Wheat Blast: Epidemiology and management of an emerging global threat. Dr. Cruz
Symptoms of Wheat Blast Under Controlled Conditions, P.K. Malaker
Wheat blast infected field in Bangladesh, 2017. Dr. Malaker

Enjoy our project!





How to use it

Under development.

---
title: " "
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    theme: united
    social: menu
    css: style2.css
    logo: logo.png
    
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(
	echo = FALSE,
	message = FALSE,
	warning = FALSE
)
library(flexdashboard)
library(prettydoc)
library(readxl)
library(tidyverse)
library(crosstalk)
library(plotly)
library(viridis)
library(gsheet)
library(leaflet.providers)
library(leaflet)
library(DT)
library(cowplot)
```



```{r load data, message=FALSE, warning=FALSE, include=FALSE}
Sys.setlocale("LC_ALL", "pt_BR.UTF-8")


mg_prod <- gsheet2tbl("https://docs.google.com/spreadsheets/d/13xAflAQ-x78Vkq0O0jUEkUxHPq5G1R4gwqzillMxoTo/edit?usp=sharing")

wb1 <- gsheet2tbl("https://docs.google.com/spreadsheets/d/1x3KKDGIwdPQdG3YE5Ekn1iExMrqV76ndJbXAgLe7dsA/edit?usp=sharing")

wb2<- gsheet2tbl("https://docs.google.com/spreadsheets/d/1W5nJWJwQJRKyUfWeiQNNA2xdQUsbEeCX6g6FH_i7i4o/edit#gid=532754791")


wb_all <- wb2 %>%
  filter(labcode != 0) %>%
    mutate(id = case_when(
    is.na(py_binomial) ~ "No",
    TRUE ~ "Yes"
  )) %>%
  mutate(wheat = case_when(
    host_binomial == "Triticum aestivum" ~ "Wheat",
    TRUE ~ "Non-wheat"
  ))

wb_all <- wb_all %>% 
  dplyr::select(labcode, year, state_province, county_municipality, lat, lon, host_binomial, py_binomial,  py_lineage, primer, seq_locus, wheat, DNA_extraction)

set.seed(1000)
wb_all$lat <- round(jitter(wb_all$lat, factor = 1, amount = 0.001), 4)
wb_all$lon <- round(jitter(wb_all$lon, factor = 1, amount = 0.001), 4)

```



```{r all table, echo=FALSE}
sd <- SharedData$new(wb_all)
```


Dashboard 
============


Column {.sidebar}
-------------------------------------



### Quick filter


```{r}
filter_slider("year", "Select years", sd, ~year)
filter_checkbox("wheat", "Host group", sd, ~wheat, inline = TRUE)
filter_select("host", "Host binomial", sd, ~host_binomial)
filter_checkbox("DNA_extraction", "DNA extracted?", sd, ~ DNA_extraction, inline = TRUE)
filter_select("py_species", "Pyricularia sp.", sd, ~py_binomial)


```


Column {.tabset}
-------------------------------------


###  Map strains

```{r}
library(RColorBrewer)
library(htmltools)
pal <- colorFactor("Set2", domain = c("Wheat", "Non-wheat"))

leaflet(data = sd, width = "100%") %>%
 # setView(-46.8, -20.40, zoom = 7) %>%
  addProviderTiles("Esri.WorldImagery", group = "Aerial") %>%
  addProviderTiles("OpenTopoMap", group = "Terrain") %>%
  addScaleBar("bottomright") %>%
  addProviderTiles(providers$CartoDB.Voyager, group = "Default") %>%
  addLayersControl(
    baseGroups = c("Default", "Aerial", "Terrain"),
    overlayGroups = "Blast pathogen",
    options = layersControlOptions(collapsed = T)
  ) %>%
  addCircleMarkers(
  
    group = "wheat",
    radius = 6,
    fillOpacity = 1,
    weight = 0.5,
    label = paste(wb_all$host, "- Details"),
    fillColor = ~ pal(wheat),

    popup = paste(
      "

Isolate details

", "Code:", wb_all$"labcode", "
", "Host:", wb_all$"host_binomial", "", "
", "City:", wb_all$"county_municipality", "
", "Year:", wb_all$"year", "
", "DNA extraction:", wb_all$"DNA_extraction", "
", "Species:", wb_all$"py_binomial", "
", "Lineage:", wb_all$"py_lineage", "
" ) ) %>% addLegend("bottomleft", pal = pal, values = ~wheat, title = "Host", opacity = 1 ) %>% addMeasure( position = "bottomleft", primaryLengthUnit = "meters", primaryAreaUnit = "sqmeters", activeColor = "#3D535D", completedColor = "#7D4479") %>% addEasyButton(easyButton( icon="fa-globe", title="Zoom to Level 3", onClick=JS("function(btn, map){ map.setZoom(3); }"))) ``` ### View grid display ```{r} datatable(sd, extensions = c("Buttons", "ColReorder"), escape = TRUE, rownames = FALSE, class = "cell-border stripe", options = list( dom = "Bfrtip", buttons = c("excel", "pdf"), deferRender = TRUE, scrollY = 50, pageLength = 15, scroller = TRUE, colReorder = TRUE ) ) ``` About this dashboard ============ Column {.sidebar} ------------------------------------- ### Participants
Dr. Emerson Del Ponte MSc. João Ascari MSc. Ignácio Cazón Dr. Mark Farman Column {.tabset} ------------------------------------- ### About this project

What is Pyblastr?

> A database of information on characterized fungal strains of Pyricularia, the genus name for a complex of species that cause blast symptoms on both leaves and reproductive organs of agriculturally important Poacea (eg. wheat blast) and grasses in general, including turfgrass.

What is it useful for?

>The dashboard contains tabular and graphical elements for displaying information for each georeferenced strain, thus allowing to track temporal and spatial spread of species and host-specific lineages (or pathotypes) isolated from both wheat and grasses.

Who are we?

>Pyblastr was created during the conduction of PhD research projects at the Plant Disease Epidemiology Laboratory (Dr. Del Ponte) in collaboration with Dr. Farman (University of Kentucky). The first version was released on February 2020 and is expected to be continuously updated.

May I contribute data?

>Yes! definitely this is an open database that we would like to extend its utility by allowing other researches to contribution data to populate the global map. Get in touch (delponte@ufv.br) to receive instructions on how to participate and include the data in a Google sheets.

Key wheat blast resources

Reviews [Wheat blast diseases: danger on the move](https://link.springer.com/article/10.1007/s40858-017-0159-z) [Wheat blast: past, present and future](https://www.annualreviews.org/doi/abs/10.1146/annurev-phyto-080417-050036) [Wheat blast: from its origins in South America to its emergence as a global threat](https://bsppjournals.onlinelibrary.wiley.com/doi/full/10.1111/mpp.12747) Websites [Open Wheat Blast Initiative](http://openwheatblast.net/) [Cimmyt](https://www.cimmyt.org/news/what-is-wheat-blast/) [CABi](https://www.cabi.org/isc/datasheet/121970) Webinars [Wheat Blast: Epidemiology and management of an emerging global threat. Dr. Cruz ](https://www.youtube.com/watch?v=gf1UFz926og&t=1188s) [Symptoms of Wheat Blast Under Controlled Conditions, P.K. Malaker](https://www.youtube.com/watch?v=9KfqahgmASE) [Wheat blast infected field in Bangladesh, 2017. Dr. Malaker](https://www.youtube.com/watch?v=pnD3qCRruK8)
Enjoy our project!





### How to use it Under development.